{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_wine\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9629629629629629"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=0)\n",
    "rfc = RandomForestClassifier(random_state=0)\n",
    "clf = clf.fit(x_train, y_train)\n",
    "rfc = rfc.fit(x_train, y_train)\n",
    "score_c = clf.score(x_test, y_test)\n",
    "score_r = rfc.score(x_test, y_test)\n",
    "\n",
    "score_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9629629629629629"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777779\n",
      "0.865032679738562\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "label = \"RandomForest\"\n",
    "for model in [RandomForestClassifier(n_estimators=25), DecisionTreeClassifier()]:\n",
    "    score = cross_val_score(model, wine.data, wine.target, cv=10)\n",
    "    print(score.mean())\n",
    "    plt.plot(range(1,11), score, label = label)\n",
    "    plt.legend()\n",
    "    label = \"DecisionTree\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfc_1 = []\n",
    "clf_1 = []\n",
    "\n",
    "for i in range(10):\n",
    "    rfc = RandomForestClassifier(n_estimators=25)\n",
    "    rfc_s = cross_val_score(rfc, wine.data, wine.target, cv=10).mean()\n",
    "    rfc_1.append(rfc_s)\n",
    "    \n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf_s = cross_val_score(clf, wine.data, wine.target, cv=10).mean()\n",
    "    clf_1.append(clf_s)\n",
    "    \n",
    "plt.plot(range(1,11),rfc_1,label = \"Random Forest\")\n",
    "plt.plot(range(1,11),clf_1,label = \"Decision Tree\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9888888888888889 32\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "superpa = []\n",
    "for i in range(60):\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1,n_jobs=-1)\n",
    "    rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10).mean()\n",
    "    superpa.append(rfc_s)\n",
    "print(max(superpa),superpa.index(max(superpa)))\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(1,61),superpa)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看随机森林内部决策树的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=20, random_state=2)\n",
    "rfc = rfc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1872583848"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.estimators_[0].random_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1872583848\n",
      "794921487\n",
      "111352301\n",
      "1853453896\n",
      "213298710\n",
      "1922988331\n",
      "1869695442\n",
      "2081981515\n",
      "1805465960\n",
      "1376693511\n",
      "1418777250\n",
      "663257521\n",
      "878959199\n",
      "854108747\n",
      "512264917\n",
      "515183663\n",
      "1287007039\n",
      "2083814687\n",
      "1146014426\n",
      "570104212\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(rfc.estimators_)):\n",
    "    print(rfc.estimators_[i].random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_features='auto', random_state=1872583848)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.estimators_[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通过有放回的抽样构建不同的分类器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过有放回的随即抽样大概会获得60%的数据进行训练，我们可以用那些没有被抽取到的数据（袋外数据）用作测试。 通过oob_score参数获得测试分数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9550561797752809"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=25, oob_score=True)\n",
    "rfc.fit(wine.data, wine.target)\n",
    "\n",
    "rfc.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814814814814815"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=25)\n",
    "rfc = rfc.fit(x_train, y_train)\n",
    "rfc.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.17068594, 0.03787218, 0.01785027, 0.04574399, 0.03594302,\n",
       "       0.03196958, 0.2340456 , 0.01710557, 0.02496142, 0.13991445,\n",
       "       0.05117002, 0.04581982, 0.14691814])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  7,  1, ...,  7,  2,  1],\n",
       "       [ 3, 12, 13, ..., 12, 12, 12],\n",
       "       [11, 11,  1, ...,  7,  4,  9],\n",
       "       ...,\n",
       "       [ 9,  7,  1, ...,  7,  2,  1],\n",
       "       [ 3, 11, 13, ..., 12, 12, 12],\n",
       "       [11,  3,  4, ...,  8,  7,  4]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.apply(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 1, 0, 0, 2, 0, 2, 1, 2, 0, 0, 2, 1, 1, 0, 0, 2, 1, 1,\n",
       "       0, 0, 0, 1, 0, 2, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 2, 0, 1, 2,\n",
       "       1, 1, 2, 1, 2, 0, 0, 1, 0, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04, 0.96, 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.28, 0.64, 0.08],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [0.08, 0.92, 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.8 , 0.16, 0.04],\n",
       "       [0.04, 0.12, 0.84],\n",
       "       [0.84, 0.04, 0.12],\n",
       "       [0.  , 0.04, 0.96],\n",
       "       [0.04, 0.76, 0.2 ],\n",
       "       [0.  , 0.  , 1.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.76, 0.24, 0.  ],\n",
       "       [0.  , 0.08, 0.92],\n",
       "       [0.  , 0.88, 0.12],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.56, 0.44, 0.  ],\n",
       "       [0.  , 0.  , 1.  ],\n",
       "       [0.04, 0.84, 0.12],\n",
       "       [0.  , 0.96, 0.04],\n",
       "       [0.92, 0.04, 0.04],\n",
       "       [0.8 , 0.2 , 0.  ],\n",
       "       [0.96, 0.04, 0.  ],\n",
       "       [0.  , 0.88, 0.12],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.  , 0.16, 0.84],\n",
       "       [0.92, 0.04, 0.04],\n",
       "       [0.96, 0.  , 0.04],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.04, 0.96, 0.  ],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [0.16, 0.68, 0.16],\n",
       "       [0.96, 0.04, 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.04, 0.96, 0.  ],\n",
       "       [0.04, 0.96, 0.  ],\n",
       "       [0.  , 0.  , 1.  ],\n",
       "       [0.88, 0.12, 0.  ],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [0.  , 0.08, 0.92],\n",
       "       [0.04, 0.92, 0.04],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [0.  , 0.  , 1.  ],\n",
       "       [0.  , 0.76, 0.24],\n",
       "       [0.  , 0.16, 0.84],\n",
       "       [0.96, 0.04, 0.  ],\n",
       "       [1.  , 0.  , 0.  ],\n",
       "       [0.  , 1.  , 0.  ],\n",
       "       [0.92, 0.08, 0.  ],\n",
       "       [0.04, 0.2 , 0.76]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基学习器必须获得超过随即分类的准确率即50%，否则随即森林效果会很差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.special import comb\n",
    "\n",
    "x = np.linspace(0,1,20)\n",
    "y = []\n",
    "for epsilon in np.linspace(0,1,20):\n",
    "    E = np.array([comb(25,i)*(epsilon**i)*((1-epsilon)**(25-i))\n",
    "        for i in range(13,26)]).sum()\n",
    "    y.append(E)\n",
    "\n",
    "plt.plot(x,y,\"o-\",label=\"when estimators are different\")\n",
    "plt.plot(x,x,\"--\",color=\"red\",label=\"if all estimators are same\")\n",
    "plt.xlabel(\"individual estimator's error\")\n",
    "plt.ylabel(\"RandomForest's error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
